首页|New Findings on Artificial Intelligence Described by Investigators at University of Jinan (State of Charge Estimation for Commercial Li-ion Battery Based On Sim ultaneously Strain and Temperature Monitoring Over Optical Fiber Sensors)
New Findings on Artificial Intelligence Described by Investigators at University of Jinan (State of Charge Estimation for Commercial Li-ion Battery Based On Sim ultaneously Strain and Temperature Monitoring Over Optical Fiber Sensors)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Artificial Intelligen ce have been presented. According to news originating from Guangzhou, People's R epublic of China, by NewsRx correspondents, research stated, "The combination of artificial intelligence methods and multisensory is crucial for future intellig ent battery management systems (BMSs). Among multisensing technologies in batter ies, simultaneously monitoring the strain and temperature is essential to determ ine the batteries' safety and state of charge (SoC)." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from the University of J inan, "However, the combination still faces a few challenges, such as obtaining multisensing parameters with only one simple and easy-to-fabricate sensor, and h ow to use artificial intelligence and measurement parameters such as strain and temperature for effective modeling. To address these, we propose a novel sensing technique based on a compact dual-diameter fiber Bragg gratings (FBGs) sensor c apable of being attached to the surface of a working lithium-ion pouch cell to s imultaneously monitor the battery's surface strain and temperature. Then, based on the collected data of strain and temperature, we have constructed deep neural network (DNN) models with different inputs to realize accurate battery SoC esti mation with high resistance to electromagnetic interference. Based on our DNN mo dels, the experimental results show that strain and temperature information can be used as supplementary parameters for improved SoC estimation (accuracy increa sed from 97.40% to 99.94%). Meanwhile, we also find t hat by just using the strain and temperature information obtained by the optical fiber sensor, the SoC estimation can be achieved without the voltage and curren t inputs."
GuangzhouPeople's Republic of ChinaA siaArtificial IntelligenceEmerging TechnologiesMachine LearningUniversit y of Jinan
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